In this section we study how GPU hardwares affect
the query performance on GPUs. As GPU's peak performance continues to grow, we examine how much
benefit analytical queries can get from the advancement of GPU hardwares.
Since query performance on GPUs is affected by the GPU hardware
and the PCIe bandwidth, we explore the impact of these two factors
independently.


\begin{figure}
\centering
\includegraphics[width=1.2in, height=2.4in, angle=270]{graph/model/improve.ps}
\vspace{-0.15in}
\caption{Normalized SSBM performance on different GPUs}
\label{fig:modelnorm}
\vspace{-0.2in}

\end{figure}

\begin{figure}
\centering
\includegraphics[width=1.2in, height=2.4in, angle=270]{graph/model/pin.ps}
\vspace{-0.15in}
\caption{Normalized SSBM query performance using unpinned and pinned host memory}
\label{fig:modelpcie}
\vspace{-0.2in}

\end{figure}


We first study the impact of GPU hareware on query performance. 
We run SSBM queries on three different NVIDIA GPUs: GTX 480, 580 and 680.
GTX 480 and 580 only support PCIe 2.0, while GTX 680 supports PCIe 3.0 which
doubles the PCIe bandwidth.
We measure the bandwidth for GTX 680 and find that it has the same transfer bandwidth
as GTX 480 and GTX 580 when using pageable memory.
In this case we use pageable memory when running queries on these GPUs such that the
performance different only comes from the differences in the internal GPU hardwares. 
We normalize the performance based on query performance on GTX 480.
The result is shown in Figure \ref{fig:modelnorm}.
As shown in the figure, the query performance does improve with the
advancement of GPU hardwares. However, the performance gain is much smaller (less 10\%)
compared to the improvement of GPU's peak performance (more than 2 times from GTX 480 to 680).
The reason is that data warehouse queries are mainly bounded by device memory accesses.
They cannot benefit much from increased computation power.

To examine the impact of PCIe bandwidth on query performance, we focus on GTX 680
and compare running SSBM queries on GTX 680 with pinned host memory and pageable
host memory independently. When we pin the host memory, the PCIe transfer bandwidth
doubles. We normalize the query performance on SSBM performance with pageable 
memory and the result is shown in Figure \ref{fig:modelpcie}.
As shown in the figure, the performance of most queries increase significantly
for most queries since their execution time are dominated by PCIe data transfer.


\begin{figure}
\centering
\includegraphics[width=1.2in, height=2.4in, angle=270]{graph/model/predict.ps}
\vspace{-0.15in}
\caption{Estimated SSBM performance with different GPU hardware configurations}
\label{fig:modelpredict}
\vspace{-0.2in}

\end{figure}

To predict the possible impact of the advancement of GPU hardwares on query performance,
we use our model, which has been proved effective in estimating query performance on GPUs,
to estimate the query performance with different GPU hardware configurations.
We double PCIe transfer bandwidth and GPU device memory bandwidth
 independently based on GTX 580's hardware parameters
to see how the SSBM performance change.

The result is shown in Figure \ref{fig:modelpredict}.
Doubling the device bandwidth doesn't improve the query performance much
as most queries are still dominated by PCIe data transfer.
In the real world scenario, the bandwidth of GPU device memory
grows at a much slower pace compared to the improvement of its peak performance. 
In this case,
the performance of data warehouse queries is not likely to benefit much
from the advancement of GPU hardwares. 

